Image-based diagnostic systems

ABSTRACT

A system for diagnosing a heart condition comprises an imaging system ( 102 ) arranged to acquire two images of the heart at respective points in the cardiac cycle, and locating means, which may be manually operated or automatic, for locating a series of pairs of points on the images. Each pair of points indicates the respective positions of a single part of the heart in the two images. The system further comprises a processor ( 108 ) arranged to calculate from the positions of said pairs of points a value of at least one parameter of the deformation of the heart. It may further be arranged to compare the value of the at least one parameter with reference data to generate a diagnostic output.

CROSS REFERENCE TO RELATED PATENT APPLICATIONS

This Application claims priority to and is a continuation of U.S. Pat.No. 10,959,698, filed Dec. 12, 2018, which claims priority to and is anational phase of PCT/GB2017/051720, filed Jun. 13, 2017, which claimspriority to GB 1610269.1, filed Jun. 13, 2016, each of which areincorporated herein by reference in their entirety.

FIELD OF THE INVENTION

The present invention relates to systems for analysing medical images,for example of the heart, and for measuring parameters of the imagedsubject. It has application in echocardiography, but also with otherimaging modalities such as X-ray computer tomography (CT), magneticresonance imaging (MRI), and positron emission tomography (PET).

BACKGROUND TO THE INVENTION

Echocardiography is widely used as a method of imaging the heart. Ituses a series of rapidly acquired pulse-echo ultrasound images to buildup, for example, a real time video image of the heart. The images aretypically two dimensional (2D) and the images are typically analysedvisually by a skilled clinician, although computer analysis of theimages is known, for example from U.S. Pat. No. 8,077,944. In stressechocardiography, the heart is imaged in a rest condition, i.e. when thesubject is at rest, and under a stress condition, for example afterexercise. The function of the heart in the two conditions can becompared to provide information on how it responds to stress. If it isnot appropriate for the subject to be exercised, then stress can beinduced or simulated, for example by injecting a stimulant such asdobutamine into the subject. Dobutamine stress echo (DSE) is widely usedin diagnosing coronary artery disease (CAD).

SUMMARY OF THE INVENTION

The present invention provides a system for measuring deformation of theheart, for example for diagnosing a heart condition, the systemcomprising an imaging system arranged to acquire two images of the heartat respective points in the cardiac cycle. The system may compriselocating means for locating a series of pairs of points on the images,each pair of points indicating the respective positions of a single partof the heart in the two images. The system may comprise processing meansarranged to calculate, for example from the positions of said pairs ofpoints, a value of at least one parameter of the deformation of theheart.

The processing means may be arranged to compare the value of at leastone parameter with reference data to generate a diagnostic output.

The at least one parameter may include any one or more of: adisplacement in at least one direction of a part of the heart; a mean,for all of said parts of the heart, of the displacements in at least onedirection; the sum of displacements in two different directions, forexample the longitudinal and radial directions, for at least one of saidparts of the heart; the mean, for all of said parts of the heart, ofthat sum of displacements; and the principle transformation which isdescribed in more detail below.

The locating means may comprise a user input device arranged to enable auser to locate said pairs of points in the images and to record thepositions of said pairs of points, for example by recording thecoordinates of each of the points in a two dimensional coordinatesystem.

The imaging system may be arranged to store the images as respectiveimage data sets, and the locating means may be arranged to process theimage data sets to determine the locations of said pairs of points andto record the positions of said pairs of points.

The at least one parameter may comprise a plurality of parameters andthe processing means may be arranged to compare the value of each of theparameters with a respective reference value.

The system may be arranged to acquire a further set of two images of theheart at respective points in a cardiac cycle, with heart in a secondcondition, which is different from its condition when the first set oftwo images are acquired. For example one of the conditions may be a restcondition when the subject is at rest and one of the conditions may be astress condition when the subject is under stress. Each of theparameters may be determined once for each set of images. One or morefurther parameters may be defined which combine data from the two setsof images. For example the difference in the value of one of theparameters, between the two sets of images, may be used as a furtherparameter.

The processing means may be arranged to define a decision tree forgenerating the diagnostic output from the values of the parameters. Thedecision tree may include a plurality of decision points. Each decisionpoint may define a reference value of one of the parameters. For exampleone of the decision points may define a reference value of the principaltransformation, and/or one of the decision points may define a value ofthe shear transformation as described in more detail below, and/or oneof the decision points may define a reference value of the differencebetween the principal transformation in the two different conditions ofthe heart. Systems for building decision trees from training data arewell known, such as C4.5 and J48.

The invention further provides a method of measuring deformation of theheart, for example for diagnosing a heart condition, the methodcomprising acquiring two images of the heart at respective points in thecardiac cycle, locating a series of pairs of points on the images, eachpair of points indicating the respective positions of a single part ofthe heart in the two images. The method may further comprisecalculating, for example from the positions of said pairs of points, atleast one parameter of the deformation of the heart. The method mayfurther comprise comparing the at least one parameter with referencedata to generate a diagnostic output.

The invention further provides a method of producing a system fordiagnosing a heart condition, the method including analysing a set ofimages, wherein each of the images has a diagnostic outcome associatedwith it, the method including calculating a value of the at least oneparameter for each of the images, analysing the values and thediagnostic outcomes to determine a relationship or correlation betweenthe two.

The method may further comprise using machine learning to develop adecision tree for generating the diagnostic output from the values ofthe parameters. The method may be performed on a computer system orprocessor system, which may form part of an imaging system, or maycomprise a separate computer.

The diagnostic output may relate to a variety of cardiac conditions,such as coronary artery disease (CAD), or mitral regurgitation, orhypertrophic cardiomyopathy.

The imaging system may comprise an echocardiography system, or it may bean X-ray imaging system such as an X-ray computer tomography (CT)scanner, magnetic resonance imaging (MRI) scanner, or a positronemission tomography (PET) scanner.

The system or method may further comprise, in any workable combination,any one or more features or steps of the preferred embodiments of theinvention, as will now be described with reference to the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view of a system according to an embodiment of theinvention;

FIG. 2 shows schematically a four chamber view of a heart;

FIG. 3 is a flow diagram showing the main steps of a diagnostic methodperformed by the system of FIG. 1 ;

FIG. 4 is a schematic view of a comparison of two images performed bythe system of FIG. 1 ;

FIGS. 5 a and 5 b show a sample image at two stages of the analysis asperformed by the system of FIG. 1 ;

FIGS. 6 a and 6 b show the results of analysis of sample data obtainedusing the system of Figure; and

FIG. 7 is a flow diagram showing the algorithm used to produce theresults of FIGS. 6 a and 6 b.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring to FIG. 1 , an echocardiography system 100 comprises atransducer array 102 arranged to be located close to the body of thepatient 104, typically as close to the heart as possible, a processingunit 106 which includes a processor 108 which may be a digitalelectronic processor, a memory 110 such as a hard disk, and a display112, such as a flat screen monitor or LED display. The system mayfurther include a user input device, for example a touchscreen 114integrated into the display 112, which provides a user input allowing auser to provide inputs to the system 100. Other user inputs such as amouse, touchpad or keyboard may of course be used. The processor unit106 is connected to the transducer array 102 and is arranged to controlthe transducer array as a phased array so as to emit an ultrasound beamwhich scans across the patient in a series of pulses, and detectreflected ultrasound from the heart from each pulse. One scan of theheart builds up a single image, and the scan is repeated at typically 25to 50 images per second to build up a real time video image of the heartshowing its movement during the cardiac cycle. Each image may be storedin the memory 110 as an image data set which may comprise, for example,intensity values for each of the pixels of which the image is made up.

While the system is described in general terms above, suitableechocardiography systems include, for example the Philips Epic iE33, GEvivid e9, or portable systems such as the Philips CX50, or hand heldsystems.

The process of echocardiography is well known and will not be describedin detail. There are several different imaging methods, but twodimensional imaging may be used. It is known to provide images onseveral different planes through the heart, which show different aspectsof the four main chambers of the heart, the left ventricle (LV), rightventricle (RV), left atrium (LA) and right atrium (RA). Such viewsinclude, for example, an apical four chamber view, apical two or threechamber views and parasternal long and short axis views. In each case,while a single still image can be obtained, typically a series of viewsis acquired over the cycle of the heart so that its movement can berecorded and analysed.

Referring to FIG. 2 , if the images are four chamber apical views, theyshow a 2D plane of the heart 200 showing the left ventricle 202, theright ventricle 204, the left atrium 206 and the right atrium 208. Theplane includes the long axis 210 of the LV, also extends through theapex 212 of the LV, the lateral wall 214 of the LV and the septum 216.

Referring to FIG. 3 , the echocardiography system 100 may be arranged toacquire at step 300 a sequence of 2D images and store them in its memory110. The images may be acquired over a single cardiac cycle, and mayinclude for example between ten and 50 images covering one cycle. Theacquisition of the images can of course be carried out on a conventionalechocardiography system 100. The following analysis of the images can becarried out using the same processing unit 106 that forms part of theechocardiography system as shown in FIG. 1 . However the images may bedownloaded onto a computer, such as a laptop or PC, which has aprocessor, memory, user input and display, which operate for thispurpose in the same way as those of the control unit 106, and thefurther analysis of the images may be carried out on that computer underthe control of dedicated software.

At step 302, the images closest to end systole, i.e. maximumcontraction, and end diastole, i.e. maximum volume of the LV, may beidentified. This can be done by a user viewing all of the images on thedisplay 112 and selecting one of them as the closest to end systole andone of them as the closest to end diastole using the user input device114. This selection may be made by the user on the basis of anassessment and comparison of the volume of the LV in each of the imagesas judged by eye, or by noting the points of opening and closing of themitral valve, or using the QRS complex on an ECG plot, or by anycombination of these. This is reasonably easy for a practiced clinicianto do. Alternatively the processor 108 may be arranged to use imageprocessing techniques to identify, and measure the volume of, the LV ineach of the images, compare the volumes of the LV in the differentimages, and identify the image with the smallest LV volume as the endsystole image and the image with the largest LV volume as the enddiastole image. In either case, once the end systole and end diastoleimages have been identified, they may be identified in the memory 110,for example being marked with an appropriate flag, so that they can beselected and viewed by a user.

Referring to FIG. 4 , once the end systole and end diastole images havebeen identified, corresponding points 401 on the wall of the LV at endsystole 400 a and at end diastole 400 b may be identified at step 304. ACartesian coordinate system may also be defined, for example having avertical axis (referred to as the y axis herein) through the apex 402 ofthe LV and extending along its longitudinal axis, and a horizontal axis(referred to as the x axis herein) through the mid-point of the LV halfway between its apex 402 and its base 404. The apex 402 and the base 404may be identified by the user via the user input, or by imageprocessing. The coordinate of each of the points 400 a, 400 b on thecoordinate system may then be determined and recorded. Since the scaleof the image is known from the echocardiography system, the coordinatesof each of the points define the position of the point in the plane ofthe image, and therefore the distance between the two points in eachpair indicates the distance moved by the relevant part of the heartbetween end systole and end diastole. Again, the identification of thepoints may be done manually by a user selecting each of the points oneach of the images using the user input 114, or it may be done by imageprocessing software running on the system 100 and arranged to analysethe shapes of the LV in each of the end systole and end diastole imagesand identify specific points. These may include for each of the twoimages, for example, a point at the apex 402 a, 402 b of the LV at bothend systole and end diastole, a point 404 a, 406 a, at one side of thebase of the LV, and a point at the other side of the base of the LV, twopoints at the midpoint of the LV, two points at the start of the apex,and various intermediate points spaced between. Some of these points aredescribed in more detail below with reference to FIGS. 5 a and 5 b.

Referring to FIG. 5 a , which shows an echo image acquired with acontrast agent, in each of the images of the LV, the apex 602 of the LVcan be located as the extreme end of the LV, and the base of the LV oneach side 604, 605 can be located from the shape of the side walls. They axis can then be defined as the line passing through the apex 602 andthe midpoint between the two sides of the base 604, 605. The x axis canthen be defined as the line perpendicular to the y axis half way betweenthe apex and the midpoint between the two sides of the base. Themid-point on each side 606, 607 can be identified as the point where thex axis intersects the side wall on that side. The lower end of the apexon each side 608, 609 can also be identified where the sidewalls startto taper towards the apex 602. As mentioned above, each of these pointsmay be identified by a user. Alternatively image processing may be usedto identify them. If image processing is used, the outline of the LV isfirst identified as the boundary between the lighter area within the LVand the darker area of the myocardium forming the walls around it (orvice versa for images acquired without use of a contrast agent). Thisboundary is not sharp, but algorithms for identifying such boundariesare well known. Once the boundary has been identified, the algorithm maythen be arranged to identify the highest point (maximum y value) of theboundary as being the apex 602, and the points where the boundarychanges direction at the lower end, for example as can be seen at thepoint 605 on the right hand side of the base line in FIG. 5 a . Againalgorithms for analysing the radius and direction of curvature, and howthat changes around the boundary, can be used to identify these points,and the points 608, 609 at the lower end of the apex.

Referring to FIG. 5 b , further points on the walls of the LV can beidentified, either manually or by the processor using simple algorithms.For example these might be points on the side walls equally spaced inthe y direction between the points indicated in FIG. 5 a.

Referring back to FIG. 3 , once all of these points have beenidentified, their x and y coordinates in the Cartesian coordinate systemmay be stored in the memory 110, for example as an end systolecoordinate set including the coordinates of the points on the endsystole image and an end diastole coordinate set including thecoordinates of the points on the end diastole image. The processor maybe arranged at step 306 to calculate, from the two coordinate sets, thetransformation in geometry of the LV between end systole and enddiastole. For example the processor may be arranged to use thecoordinate sets to calculate movement in one or more directions as willnow be described in more detail.

Referring back to FIG. 4 , the processor 108 is arranged to calculate,for the deformation of the shape of the LV between end systole and enddiastole, values for various parameters that quantify the movement ofthe LV between end systole and end diastole.

The calculation may include working out how far each point has moved ineach of the x and y directions, by working out the change in position(End diastole−End systole) along both the x axis and the y axis. Thisgives a set of x axis movements Δx, with one value for eachcorresponding pair of points, as shown in FIG. 4 for the points 404 a,404 b, and a set of y axis movements Δy, again with one value for eachcorresponding pair of points. Each of these values may be a simpledistance with no indication of direction. The mean change of all thepoints in both the x axis (ΔX) and y axis (ΔY) may then be calculatedseparately so as to provide an average Δx value or x direction movementΔX, and an average Δy value or y direction movement ΔY for the entireventricle. If each of the individual movement values are purelydistance, without any indication of whether they are in the positive ornegative x or y direction, then these averages will describe the totalamount of movement, but not give an indication of the direction or ofwhether different parts of the LV wall are moving in the same directionor opposite directions.

Another parameter that maybe calculated is, for each point on the LV,i.e. each pair of points on the images, to calculate the mean of the xand y direction movements Δx and Δy, where the mean value for each pointΔxy=(Δx+Δy)/2. The mean of all the values of Δxy for all points can thenbe calculated to a value for the entire ventricle ΔXY. This calculationis similar to the calculation of shear strain and is therefore referredto herein as the shear transformation. It will be appreciated that, fora given distance of movement, this parameter will be largest formovements at 45 degrees to both of the x and y axes, and smallest formovements along one of the axes.

A further parameter that can be calculated is similar to the principaltransformation that can be calculated from x and y strain components,and is therefore referred to herein as the principal transformation,given byprincipal transformation=C1(ΔX+ΔY−√(ΔX+ΔY){circumflex over( )}2+C2ΔXY{circumflex over ( )}2)where C1 and C2 are constants. For example C1 may be ½ and C2 may be 4.These values were used in the examples described below.

This transformation is closely related to the shear transformation andtherefore tends to vary in a similar way to that parameter, but has anegative value indicating contraction of the heart. However, asindicated by the test results below, the principal transformation valuecan give a more reliable diagnosis in some cases, in particular of CAD.

It will be appreciated that each of these parameters relates to changesbetween end systole and end diastole in a single coronary cycle. Howeverin stress echocardiography, (or corresponding tests carried out withother imaging methods) there will be one value for each parameter forthe heart at rest and one value for the heart at stress. Comparing thosevalues, for example determining the difference between them, givesfurther information about heart function that can be used in diagnosis.

Once the x and y movements, and shear and principal transformationvalues have been calculated, the processor is then arranged at step 308to compare these with reference values stored in the memory 110 to makea diagnosis of one or more specific heart conditions, and to generate adiagnostic output. The output may be a simple binary output indicating apositive or negative diagnosis. The processor unit 106 may be arrangedto display the output on the display 112. Alternatively, or in addition,it may be arranged to store the output as data in association with theimages on which it was based, for example by adding output data,indicative of the diagnosis, to a file in which the images are stored.

The reference values may for example be determined by analysis of imagesof hearts some of which do and some of which do not have the specificheart conditions to determine for example threshold values which areindicative of a specific condition.

The reference values can be determined by means of a learning algorithmwhich, for example, can be run on the processor unit 106, and which usesa database of stress echo images with associated diagnoses as determinedby conventional methods, which may be stored in the memory 110.Specifically the database may include a large number of sets of images,each set comprising an end systole image and an end diastole image forboth rest condition and stress condition, together with, for each set ofimages, an associated diagnosis, such as a positive or negativediagnosis for CAD. The learning algorithm may be arranged to analyse theimages to calculate values of the various parameters described above,and then to determine the correlation between the diagnosis and thevalues of each of the various parameters.

Analysis was carried out on sample images from 70 subjects. All resultsgenerated were from an apical 4 chamber view. Firstly the values werecompared for positive and negative outcomes as determined from the DSEresults. Then the comparison was repeated with the DSE results correctedfor confirmed false positives in the DSE results.

Table 1 Shows values of the principal transformation (in mm), sheartransformation value (in mm), and mean ΔX (in mm) at rest and stress forDSE outcome (1=Pos, 2=Neg) in the Apical 4 Chamber view.

Group Statistics Std. Std. Error DSE_Result N Mean Deviation MeanStress_Prin 1.00 9 −6.8214 4.08788 1.36263 2.00 61 −8.9260 2.20018.28170 Rest_Prin 1.00 9 −7.7332 3.86497 1.28832 2.00 61 −9.3163 2.41589.30932 Rest_Shr 1.00 9 17.7267 9.16943 3.05648 2.00 61 21.5356 5.50610.70498 Stress_Shr 1.00 9 17.0074 8.06969 2.68990 2.00 61 22.2608 4.56871.58496 Rest_X 1.00 9 18.8694 11.02116 3.67372 2.00 61 21.8492 6.65078.85155 Stress_X 1.00 9 19.9334 9.80639 3.26880 2.00 61 25.8710 7.43965.95255Table 2 Shows means of Principal transformation value (in mm), Sheartransformation (in mm) and X transformation (in mm) at rest and stressfor Adjusted DSE outcome (1=Pos, 2=Neg).

Group Statistics Std. Std. Error Adjusted_DSE N Mean Deviation MeanStress_Prin 1.00 7 −4.4716 1.29120 .48803 2.00 63 −9.1203 2.24588 .28295Rest_Prin 1.00 7 −5.3352 1.21275 .45838 2.00 63 −9.5325 2.44136 .30758Rest_Shr 1.00 7 12.0645 2.74525 1.03761 2.00 63 22.0438 5.58342 .70344Stress_Shr 1.00 7 12.2348 3.81629 1.44242 2.00 63 22.6243 4.44025 .55942Rest_X 1.00 7 11.6937 2.73459 1.03358 2.00 63 22.5519 6.84823 .86280Stress_X 1.00 7 14.1727 4.81157 1.81860 2.00 63 26.3226 7.29318 .91885Table 3 Shows Independent samples T-Test for variables vs adjusted DSE.

Independent Samples Test Levene's Test for Equality of Variances Sig. FSig. t df (2-tailed) Stress_Prin Equal variances 1.705 .196 5.356 68.000 assumed Equal variances 8.240 10.596 .000 not assumed Rest_PrinEqual variances 2.355 .130 4.466 68 .000 assumed Equal variances 7.60412.377 .000 not assumed Rest_8 hr Equal variances 2.106 .151 −4.644 68.000 assumed Equal variances −7.961 12.527 .000 not assumed Stress_8 hrEqual variances .194 .661 −5.942 68 .000 assumed Equal variances −6.7157.923 .000 not assumed Rest_X Equal variances 5.696 .020 −4.136 68 .000assumed Equal variances −8.065 16.500 .000 not assumed Stress_X Equalvariances .927 .339 −4.290 68 .000 assumed Equal variances −5.963 9.395.000 not assumedMachine Learning Results

From the values of the various parameters obtained from the sample data,machine learning may be used to determine the accuracy of each parameteras an indicator of adjusted DSE outcome. Using the data above, a J48pruned decision tree with 10 fold cross validation method was used toclassify the data. The accuracy of each parameter as an indicator ofdiagnostic outcome is summarized in the tables below, in which thefollowing abbreviations used are:

-   -   TP=true positive    -   FP=false positive    -   FN=false negative    -   TN=true negative    -   PPV=positive predictive value    -   NPV=negative predictive value

TABLE 4 Accuracy of Consultant Interpretation J48 TP = 6 FN = 1 Accuracy= 94.3% FP = 3 TN = 60 Sensitivity = 85.7% PPV = 66.7% Specificity = 95%NPV = 98.4%

TABLE 5 Accuracy of Stress Principal Transformation for Adjusted DSEoutcome J48 Value = −5.95 TP = 7 FN = 0 Accuracy = 95.7% FP = 3 TN = 60Sensitivity = 100% PPV = 70% Specificity = 95.2% NPV = 100%

TABLE 6 Accuracy of Rest Principal Transformation for Adjusted DSEoutcome J48 Value = −6.92 TP = 5 FN = 2 Accuracy = 88.6% FP = 6 TN = 57Sensitivity = 71.4 PPV = 45.5% Specificity = 90.5% NPV = 96.6%

TABLE 7 Accuracy of Stress Shear Transformation for Adjusted DSE outcomeJ48 Value = 15.85 TP = 6 FN = 1 Accuracy = 95.7% FP = 2 TN = 61Sensitivity = 85.7% PPV = 85.7 Specificity = 96.8% NPV = 98.4

TABLE 8 Accuracy of Rest Shear Transformation for Adjusted DSE OutcomeJ48 Value = 15.35 TP = 5 FN = 2 Accuracy = 91.4% FP = 4 TN = 59Sensitivity = 71.4 PPV = 55.6% Specificity = 93.7% NPV = 96.7%

Then from all of the variables, using machine learning, a decision treewhich is shown in FIG. 7 was derived to provide accurate diagnosis fromthe data. The decision tree defines a series of decision points, each ofwhich defines a reference or threshold value of a parameter. Thedecision tree outlines a simple algorithm which operates as follows.Firstly the principal transformation of the LV as described above isdetermined for the stress condition of the heart. If the transformationis less than −5.95 mm (i.e. a negative value with magnitude greater than5.95 mm) then the diagnosis is negative. If the value is greater than−5.95 mm then difference in principal transformation between rest andstress conditions is greater than 12.278053 mm then the diagnosis isnegative, but if it is less than that distance, the diagnosis ispositive. It will be appreciated that the structure of the decisiontree, and the reference or threshold values at each decision point inthe decision tree, will depend on the diagnosis that is to be made.

The decision tree was then used on the sample data to test its accuracyand the outcome is given below.

Machine Learning on all Variables

TABLE 9 Accuracy of J48 decision tree for Adjusted DSE Outcome J48 Value= FIG. 1 Algorithm TP = 5 FN = 2 Accuracy = 100% FP = 4 TN = 59Sensitivity = 100% PPV = 100% Specificity = 100% NPV = 100%

To test whether a 2 chamber view could be used instead of a 4 chamberview in a similar diagnostic system, two chamber views corresponding toeach of the four chamber views in the sample data were analysed in thesame way to derive the same parameters of principal transformation,shear transformation, and radial (X) and longitudinal (Y) movements.

TABLE 10 Intraclass correlation coefficient of translation measurementparameters between 4 chamber and 2 chamber views. Prin transformation0.84 Radial displacement (ΔX) −0.23 Shear transformation 0.36Longitudinal displacement (ΔY) −0.532

A very significant result is the similarity in principal transformationvalues between the 4 and 2 chamber view. This implies that not only isthe principal vector a sensitive parameter for detecting disease, italso implies that it gives a 3D functional assessment from a 2D view

TABLE 11 Principal transformation value stats for other Disease CohortsPrincipal transformation Principal transformation 2ch 4ch HCM −4.5 −3.6Mitral Regurgitation −6.0 −6.1 Healthy −7.7 −7.5

Table 11 illustrates that the principal transformation is reduced inother disease cohorts (hypertrophic cardiomyopathy (HCM) and mitralregurgitation) implying it is also sensitive at detecting hypertrophy,cardiomyopathies and valve disorders. Notice that the principal vectorwas reduced in both the 4 chamber and the 2 chamber views indicatingthat it is sensitive at detecting abnormalities in the heart from just asingle plane. Specifically with regard to HCM where the hypertrophyoccurred only in the 4 chamber view yet the principal transformation wasstill significantly reduced in the 2 chamber view.

It will be appreciated that analysis of images in just one plane can beused to diagnose a range of diseases, and that various differentparameters can be used to develop a decision tree which provides moreaccurate diagnosis than a single parameter.

The invention claimed is:
 1. A system for diagnosing a heart conditioncomprising: an imaging system arranged to acquire two images of theheart at respective points in a cardiac cycle; locating means forlocating positions of a series of pairs of points on the images, eachpair of points indicating respective positions of a single part of theheart in the two images; and processing means arranged to calculate fromthe positions of said pairs of points a value of at least one parameterof a deformation of the heart, and to compare the value of the at leastone parameter with reference data to generate a diagnostic output,wherein the at least one parameter of the deformation of the heartcomprises principal transformation and/or shear transformation.
 2. Asystem according to claim 1 wherein the at least one parameter includesa displacement in at least one direction.
 3. A system according to claim2 wherein the displacement is a mean of the displacements for all ofsaid parts of the heart in at least one direction.
 4. A system accordingto claim 1 wherein the locating means comprises a user input devicearranged to enable a user to locate said pairs of points and to recordthe positions of said pairs of points.
 5. A system according to claim 1wherein the imaging system is arranged to store the images as respectiveimage data sets; and the locating means is arranged to process the imagedata sets to determine the locations of said pairs of points and torecord the positions of said pairs of points.
 6. A system according toclaim 1 wherein the at least one parameter comprises a plurality ofparameters and the processing means is arranged to compare the value ofeach of the plurality of parameters with a respective reference value.7. A system according to claim 6 wherein the processing means isarranged to use machine learning to define a decision tree forgenerating the diagnostic output from the values of the plurality ofparameters.
 8. A system for measuring deformation of a heart, the systemcomprising: an imaging system arranged to acquire two images of theheart at respective points in a cardiac cycle; locating means forlocating positions of a series of pairs of points on the images, eachpair of points indicating respective positions of a single part of theheart in the two images; and processing means arranged to calculate fromthe positions of said pairs of points a value of at least one parameterof the deformation of the heart, wherein the at least one parameter ofthe deformation of the heart comprises principal transformation and/orshear transformation.
 9. A method of measuring deformation of a heart,the method comprising: acquiring two images of the heart at respectivepoints in a cardiac cycle; locating positions of a series of pairs ofpoints on the two images, each pair of points indicating respectivepositions of a single part of the heart in the two images; calculatingfrom the positions of said pairs of points a value of at least oneparameter of the deformation of the heart, wherein the at least oneparameter comprises principal transformation and/or sheartransformation; and comparing the value of the at least one parameterwith reference data to generate a diagnostic output.
 10. A methodaccording to claim 9 wherein the at least one parameter includes adisplacement in at least one direction.
 11. A method according to claim10 wherein the displacement is a mean of the displacements for all ofsaid parts of the heart in at least one direction.
 12. A methodaccording to claim 9 further comprising processing image data sets todetermine respective locations of said pairs of points and recording thepositions of said pairs of points.
 13. A method according to claim 9wherein the at least one parameter comprises a plurality of parametersand comparing the value of the at least one parameter with the referencedata includes comparing a value of each of the parameters with arespective reference value.
 14. A method according to claim 9 furthercomprising using machine learning to define a decision tree forgenerating the diagnostic output from the values of the parameters.